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Handling missing or inconsistent data is a crucial part of any analysis. First, I identify the type and extent of missing or inconsistent entries. Depending on the situation, I may remove rows, fill missing values with mean/median/mode, or use forward/backward fill techniques. For inconsistent data (e.g., typos, different formats), I use data cleaning functions in Python or Excel to standardize entries. During my Data Analytics course online, I learned to apply these techniques using tools like Pandas, NumPy, and Power Query. A solid understanding of data preprocessing ensures accurate, meaningful insights and is a key skill for any aspiring analyst.
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Posted : 24/07/2025 11:52 am